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对抗样本 2013
 人类大脑是非常有限的，我们没有那么多脑容量去研究所有东西的可解释性 -- LeCun 机器学习可解释性的问题  定义不明 标准不确定 效果不清楚   共识：对抗样本不是过拟合的结果，而是没有被发现的特征（欠拟合） 攻击方法  基于梯度 限制参数变化 One Pixel Attack 利用一个图像类别的特征 基于人的感官（声屏蔽，Masking）   防御方法（效果都不好）  对抗训练 输入数据预处理 改动模型   " />
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对抗样本
2013

人类大脑是非常有限的，我们没有那么多脑容量去研究所有东西的可解释性 -- LeCun
机器学习可解释性的问题

定义不明
标准不确定
效果不清楚


共识：对抗样本不是过拟合的结果，而是没有被发现的特征（欠拟合）
攻击方法

基于梯度
限制参数变化
One Pixel Attack
利用一个图像类别的特征
基于人的感官（声屏蔽，Masking）


防御方法（效果都不好）

对抗训练
输入数据预处理
改动模型


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<meta property="og:description" content="NJU: 人工智能 项目介绍
对抗样本
2013

人类大脑是非常有限的，我们没有那么多脑容量去研究所有东西的可解释性 -- LeCun
机器学习可解释性的问题

定义不明
标准不确定
效果不清楚


共识：对抗样本不是过拟合的结果，而是没有被发现的特征（欠拟合）
攻击方法

基于梯度
限制参数变化
One Pixel Attack
利用一个图像类别的特征
基于人的感官（声屏蔽，Masking）


防御方法（效果都不好）

对抗训练
输入数据预处理
改动模型


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对抗样本
2013

人类大脑是非常有限的，我们没有那么多脑容量去研究所有东西的可解释性 -- LeCun
机器学习可解释性的问题

定义不明
标准不确定
效果不清楚


共识：对抗样本不是过拟合的结果，而是没有被发现的特征（欠拟合）
攻击方法

基于梯度
限制参数变化
One Pixel Attack
利用一个图像类别的特征
基于人的感官（声屏蔽，Masking）


防御方法（效果都不好）

对抗训练
输入数据预处理
改动模型


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            <p>NJU: 人工智能 项目介绍</p>
<h1 id="对抗样本">对抗样本<a href="#对抗样本" class="anchor" aria-hidden="true"><i class="iconfont icon-link"></i></a></h1>
<p>2013</p>
<ul>
<li>人类大脑是非常有限的，我们没有那么多脑容量去研究所有东西的可解释性 -- LeCun</li>
<li>机器学习可解释性的问题
<ul>
<li>定义不明</li>
<li>标准不确定</li>
<li>效果不清楚</li>
</ul>
</li>
<li>共识：对抗样本不是过拟合的结果，而是没有被发现的特征（欠拟合）</li>
<li>攻击方法
<ul>
<li>基于梯度</li>
<li>限制参数变化</li>
<li>One Pixel Attack</li>
<li>利用一个图像类别的特征</li>
<li>基于人的感官（声屏蔽，Masking）</li>
</ul>
</li>
<li>防御方法（效果都不好）
<ul>
<li>对抗训练</li>
<li>输入数据预处理</li>
<li>改动模型</li>
</ul>
</li>
</ul>
<!-- raw HTML omitted -->
<h1 id="风格迁移">风格迁移<a href="#风格迁移" class="anchor" aria-hidden="true"><i class="iconfont icon-link"></i></a></h1>
<ul>
<li>神经风格迁移
<ul>
<li>优化
<ul>
<li>2016 Neural Style</li>
</ul>
</li>
<li>统计
<ul>
<li>2016 AdaIN/WCT
<ul>
<li>二阶统计信息</li>
</ul>
</li>
</ul>
</li>
<li>匹配
<ul>
<li>2016 Style Swap
<ul>
<li>对内容图的每个小块，在风格图上寻找与之结构最相近的区域，替换到内容图上</li>
</ul>
</li>
</ul>
</li>
<li>统计匹配结合
<ul>
<li>2019 AvatarNet
<ul>
<li>不同区域的迁移程度不同</li>
<li>Attention</li>
</ul>
</li>
</ul>
</li>
<li>2019 WarpGAN</li>
</ul>
</li>
</ul>
<h1 id="domain-adaptation">Domain Adaptation<a href="#domain-adaptation" class="anchor" aria-hidden="true"><i class="iconfont icon-link"></i></a></h1>
<ul>
<li>Source Domain (Labeled)</li>
<li>Target Domain (Unlabeled)</li>
<li>Align the data distributions of source and target domains</li>
<li>Multi-source domain adaptation</li>
<li><a href="http://ai.bu.edu/visda-2019">Large domain discrepancy</a></li>
</ul>
<h1 id="关系数据挖掘政务数据异常检测">关系数据挖掘（政务数据异常检测）<a href="#关系数据挖掘政务数据异常检测" class="anchor" aria-hidden="true"><i class="iconfont icon-link"></i></a></h1>
<ul>
<li>五大任务
<ul>
<li>异常检测</li>
<li>关联规则学习</li>
<li>聚类</li>
<li>分类</li>
<li>回归</li>
</ul>
</li>
</ul>
<h1 id="材料与化学信息学中的深度学习应用系统">材料与化学信息学中的深度学习应用系统<a href="#材料与化学信息学中的深度学习应用系统" class="anchor" aria-hidden="true"><i class="iconfont icon-link"></i></a></h1>

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